Selected Variables

base: Code of the patient
covariates:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Levels Previously operated - Lower
- LGap
- RLL
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
outcomes_ql:
- 2Y. ODI - Score (%)
- 2Y. SRS22 - SRS Subtotal score
- 2Y. SF36 - MCS
- 2Y. SF36 - PCS
outcomes_radiology:
- 6W. Major curve Cobb angle
- 1Y. Major curve Cobb angle
- 6W. T1 Sagittal Tilt
- 1Y. T1 Sagittal Tilt
- 6W. Sagittal Balance
- 1Y. Sagittal Balance
- 6W. Global Tilt
- 1Y. Global Tilt
- 6W. Lordosis (top of L1-S1)
- 1Y. Lordosis (top of L1-S1)
- 6W. LGap
- 1Y. LGap
- 6W. Pelvic Tilt
- 1Y. Pelvic Tilt
predictive:
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Osteotomy
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Tobacco use_First Visit
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
demographic:
- Age
- Gender
- Prior Spine Surgery
- ASA classification
- 3CO
- BMI_First Visit
- Global Tilt
- Ideal LL
- Lordosis (top of L1-S1)
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
expanded:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Levels Previously operated - Lower
- LGap
- RLL
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
- SRS22 - SRS Subtotal score_First Visit
- T1 Sagittal Tilt
- Sagittal Balance
- Global Tilt
- Lordosis (top of L1-S1)
- Pelvic Tilt

Propensity Scores Common Support

## Loading required package: lattice
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## Attaching package: 'lattice'
## The following object is masked from 'package:boot':
## 
##     melanoma
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## Attaching package: 'caret'
## The following object is masked from 'package:survival':
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Model Stats

  • Treatment proportion: 0.127
  • Model Type: elastic_net
  • Accuracy: 0.8983651
  • Params: alpha: 0.4923077 lambda: 0.0016797

Model Coefficients

Bootstraping replicas: 200, confidence intervals at 95% level

Average Treatment Effects - Quality Life

Outcome: 2Y. ODI - Score (%)
Distribution:
  0%  25%  50%  75% 100% 
 -67  -27  -14   -4   40 
Model Type Y: boosting 
RMSE: 18.4627137838729 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5357143

Model Type No: boosting 
RMSE: 17.6070112099825 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.6071429

ATE (Yes-No): 0.772 (Std.Error: 2.326)
Trimmed ATE (Yes-No): 0.828 (Std.Error: 2.435)
Upper ATE (Yes-No): -0.591 (Std.Error: 2.925)
Observational differences in treatment 4.93 (Yes-No) 

   treatment  outcome
1:       Yes 35.62069
2:        No 30.69076
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 2Y. SRS22 - SRS Subtotal score
Distribution:
   0%   25%   50%   75%  100% 
-0.95  0.21  0.70  1.16  3.05 
Model Type Y: boosting 
RMSE: 0.703247813683134 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.6785714

Model Type No: boosting 
RMSE: 0.691979555657921 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5357143

ATE (Yes-No): 0.225 (Std.Error: 0.067)
Trimmed ATE (Yes-No): 0.22 (Std.Error: 0.07)
Upper ATE (Yes-No): 0.334 (Std.Error: 0.135)
Observational differences in treatment -0.024 (Yes-No) 

   treatment  outcome
1:       Yes 3.347241
2:        No 3.371548
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 2Y. SF36 - MCS
Distribution:
    0%    25%    50%    75%   100% 
-33.82  -3.72   3.85  12.91  39.74 
Model Type Y: boosting 
RMSE: 18.2094690038809 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 12.2253370419963 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.9642857

ATE (Yes-No): -1.596 (Std.Error: 0.079)
Trimmed ATE (Yes-No): -2.04 (Std.Error: 0.071)
Upper ATE (Yes-No): 9.558 (Std.Error: 0.708)
Observational differences in treatment -1.046 (Yes-No) 

   treatment  outcome
1:       Yes 45.83280
2:        No 46.87847
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 2Y. SF36 - PCS
Distribution:
    0%    25%    50%    75%   100% 
-18.94   0.61   6.57  12.42  38.99 
Model Type Y: boosting 
RMSE: 8.75377298281998 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.8214286

Model Type No: boosting 
RMSE: 9.34705399966407 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.8571429

ATE (Yes-No): 1.171 (Std.Error: 0.514)
Trimmed ATE (Yes-No): 1.548 (Std.Error: 0.528)
Upper ATE (Yes-No): -8.307 (Std.Error: 1.266)
Observational differences in treatment -2.2 (Yes-No) 

   treatment  outcome
1:       Yes 38.04520
2:        No 40.24513
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Average Treatment Effects - Radiology

Outcome: 6W. Major curve Cobb angle
Distribution:
    0%    25%    50%    75%   100% 
-72.00 -21.00 -10.63  -4.00  30.80 
Model Type Y: boosting 
RMSE: 21.1366893988911 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 13.1468502299874 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.6785714

ATE (Yes-No): -0.253 (Std.Error: 0.262)
Trimmed ATE (Yes-No): -0.111 (Std.Error: 0.255)
Upper ATE (Yes-No): -3.655 (Std.Error: 1.519)
Observational differences in treatment 3.397 (Yes-No) 

   treatment  outcome
1:       Yes 25.23682
2:        No 21.83957
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. Major curve Cobb angle
Distribution:
    0%    25%    50%    75%   100% 
-64.00 -22.47 -10.10  -3.00  22.44 
Model Type Y: boosting 
RMSE: 20.4606579214263 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 14.0988713620794 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.6428571

ATE (Yes-No): -1.136 (Std.Error: 0.238)
Trimmed ATE (Yes-No): -1.001 (Std.Error: 0.239)
Upper ATE (Yes-No): -4.044 (Std.Error: 1.46)
Observational differences in treatment 2.873 (Yes-No) 

   treatment  outcome
1:       Yes 23.74424
2:        No 20.87154
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-23.631420  -6.000000  -1.496444   1.722212  18.000000 
Model Type Y: boosting 
RMSE: 6.54048994832397 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 6.08946309021792 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -5.319 (Std.Error: 0)
Trimmed ATE (Yes-No): -5.426 (Std.Error: 0)
Upper ATE (Yes-No): -2.693 (Std.Error: 0)
Observational differences in treatment -1.028 (Yes-No) 

   treatment   outcome
1:       Yes -3.468840
2:        No -2.441266
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

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Outcome: 1Y. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-30.098675  -6.000000  -2.009266   1.134408  20.000000 
Model Type Y: boosting 
RMSE: 7.53436331085143 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.7142857

Model Type No: boosting 
RMSE: 5.92501849428631 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5714286

ATE (Yes-No): -3.398 (Std.Error: 0.543)
Trimmed ATE (Yes-No): -3.353 (Std.Error: 0.569)
Upper ATE (Yes-No): -4.187 (Std.Error: 1.201)
Observational differences in treatment -0.702 (Yes-No) 

   treatment   outcome
1:       Yes -3.307045
2:        No -2.605153
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. Sagittal Balance
Distribution:
       0%       25%       50%       75%      100% 
-194.7900  -69.0225  -27.8500    1.9650  114.1500 
Model Type Y: boosting 
RMSE: 63.1092408321018 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.7142857

Model Type No: boosting 
RMSE: 53.7765206378945 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5714286

ATE (Yes-No): -43.892 (Std.Error: 3.255)
Trimmed ATE (Yes-No): -44.361 (Std.Error: 3.405)
Upper ATE (Yes-No): -34.415 (Std.Error: 9.738)
Observational differences in treatment -8.555 (Yes-No) 

   treatment  outcome
1:       Yes 26.17093
2:        No 34.72625
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

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Outcome: 1Y. Sagittal Balance
Distribution:
      0%      25%      50%      75%     100% 
-237.470  -67.310  -30.510    5.985  109.540 
Model Type Y: boosting 
RMSE: 73.0757122069807 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5714286

Model Type No: boosting 
RMSE: 52.9599213723664 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.7857143

ATE (Yes-No): -37.127 (Std.Error: 4.449)
Trimmed ATE (Yes-No): -36.309 (Std.Error: 4.732)
Upper ATE (Yes-No): -52.294 (Std.Error: 8.166)
Observational differences in treatment -12.482 (Yes-No) 

   treatment  outcome
1:       Yes 25.00806
2:        No 37.48991
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. Global Tilt
Distribution:
     0%     25%     50%     75%    100% 
-68.620 -18.035  -6.000   1.610 149.410 
Model Type Y: boosting 
RMSE: 14.769757504468 
Params: nrounds: 50.0
max_depth: 14
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5357143

Model Type No: boosting 
RMSE: 14.6872937338084 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.6785714

ATE (Yes-No): -12.407 (Std.Error: 0.448)
Trimmed ATE (Yes-No): -12.666 (Std.Error: 0.46)
Upper ATE (Yes-No): -6.455 (Std.Error: 1.493)
Observational differences in treatment -5.722 (Yes-No) 

   treatment  outcome
1:       Yes 19.65955
2:        No 25.38121
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. Global Tilt
Distribution:
    0%    25%    50%    75%   100% 
-62.63 -16.94  -6.21   1.00  26.00 
Model Type Y: boosting 
RMSE: 15.4648373229717 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5714286

Model Type No: boosting 
RMSE: 11.6483897787846 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -13.593 (Std.Error: 1.351)
Trimmed ATE (Yes-No): -13.53 (Std.Error: 1.431)
Upper ATE (Yes-No): -14.843 (Std.Error: 1.512)
Observational differences in treatment -5.435 (Yes-No) 

   treatment  outcome
1:       Yes 20.46031
2:        No 25.89494
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. Lordosis (top of L1-S1)
Distribution:
     0%     25%     50%     75%    100% 
-94.930 -24.000  -9.635   0.140  29.000 
Model Type Y: boosting 
RMSE: 20.2872774490426 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 15.8674179095648 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5357143

ATE (Yes-No): -5.975 (Std.Error: 1.587)
Trimmed ATE (Yes-No): -6.064 (Std.Error: 1.656)
Upper ATE (Yes-No): -3.838 (Std.Error: 3.06)
Observational differences in treatment -1.998 (Yes-No) 

   treatment   outcome
1:       Yes -51.05795
2:        No -49.05961
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Lordosis (top of L1-S1)
Distribution:
     0%     25%     50%     75%    100% 
-94.630 -25.000  -8.230  -0.015  23.380 
Model Type Y: boosting 
RMSE: 22.5947910024884 
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.6071429

Model Type No: boosting 
RMSE: 15.7011649949062 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.9285714

ATE (Yes-No): -13.388 (Std.Error: 1.599)
Trimmed ATE (Yes-No): -13.293 (Std.Error: 1.68)
Upper ATE (Yes-No): -15.438 (Std.Error: 2.022)
Observational differences in treatment 0.145 (Yes-No) 

   treatment   outcome
1:       Yes -49.32812
2:        No -49.47321
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. LGap
Distribution:
      0%      25%      50%      75%     100% 
-96.1234 -24.0000  -9.1601   0.4592  78.9200 
Model Type Y: boosting 
RMSE: 20.5980085555759 
Params: nrounds: 100.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

Model Type No: boosting 
RMSE: 17.3197054268944 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.7857143

ATE (Yes-No): -5.681 (Std.Error: 0.948)
Trimmed ATE (Yes-No): -5.596 (Std.Error: 1.007)
Upper ATE (Yes-No): -7.724 (Std.Error: 1.893)
Observational differences in treatment -3.364 (Yes-No) 

   treatment  outcome
1:       Yes 11.29544
2:        No 14.65980
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. LGap
Distribution:
      0%      25%      50%      75%     100% 
-94.8082 -25.0000  -8.5790  -0.1606  22.0800 
Model Type Y: boosting 
RMSE: 24.4436751231924 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.9285714

Model Type No: boosting 
RMSE: 15.6874869377557 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -13.325 (Std.Error: 0.866)
Trimmed ATE (Yes-No): -13.179 (Std.Error: 0.918)
Upper ATE (Yes-No): -16.447 (Std.Error: 0.833)
Observational differences in treatment -2.197 (Yes-No) 

   treatment  outcome
1:       Yes 11.43743
2:        No 13.63423
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. Pelvic Tilt
Distribution:
    0%    25%    50%    75%   100% 
-36.41  -8.59  -2.53   2.11  14.42 
Model Type Y: boosting 
RMSE: 10.0995449989016 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.7142857

Model Type No: boosting 
RMSE: 7.56723206016123 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -3.416 (Std.Error: 0.76)
Trimmed ATE (Yes-No): -3.378 (Std.Error: 0.793)
Upper ATE (Yes-No): -4.4 (Std.Error: 1.538)
Observational differences in treatment -3.393 (Yes-No) 

   treatment  outcome
1:       Yes 18.57233
2:        No 21.96521
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Pelvic Tilt
Distribution:
     0%     25%     50%     75%    100% 
-26.620  -7.160  -2.205   1.945  23.000 
Model Type Y: boosting 
RMSE: 10.9277782855396 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.9285714

Model Type No: boosting 
RMSE: 6.79640772025747 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -7.714 (Std.Error: 0.428)
Trimmed ATE (Yes-No): -7.938 (Std.Error: 0.45)
Upper ATE (Yes-No): -2.959 (Std.Error: 0.9)
Observational differences in treatment -3.96 (Yes-No) 

   treatment  outcome
1:       Yes 18.76625
2:        No 22.72589
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Average Treatment Effects - Complications

Outcome: complication
Distribution:
Proportion 
 0.2939759 
Model Type Y: boosting 
Accuracy: 0.577777777777778 
Params: nrounds: 50.0
max_depth: 4
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
Accuracy: 0.716232506479082 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): 0.142 (Std.Error: 0.044)
Trimmed ATE (Yes-No): 0.141 (Std.Error: 0.046)
Upper ATE (Yes-No): 0.159 (Std.Error: 0.073)
Observational differences in treatment 0.044 (Yes-No) 

   treatment   outcome
1:       Yes 0.3333333
2:        No 0.2891892
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: Mechanical complications
Distribution:
Proportion 
 0.2120482 
Model Type Y: boosting 
Accuracy: 0.801666666666667 
Params: nrounds: 50.0
max_depth: 5
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
Accuracy: 0.789189189189189 
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -0.068 (Std.Error: 0.025)
Trimmed ATE (Yes-No): -0.076 (Std.Error: 0.025)
Upper ATE (Yes-No): 0.157 (Std.Error: 0.06)
Observational differences in treatment -0.014 (Yes-No) 

   treatment   outcome
1:       Yes 0.2000000
2:        No 0.2135135
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: Infectious complications
Distribution:
Proportion 
0.06024096 
Model Type Y: boosting 
Accuracy: 0.913333333333333 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
Accuracy: 0.943275330124645 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): 0.069 (Std.Error: 0.019)
Trimmed ATE (Yes-No): 0.071 (Std.Error: 0.02)
Upper ATE (Yes-No): 0.009 (Std.Error: 0.014)
Observational differences in treatment 0.032 (Yes-No) 

   treatment    outcome
1:       Yes 0.08888889
2:        No 0.05675676
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: Peripheral neurologic complications
Distribution:
Proportion 
0.04819277 
Model Type Y: boosting 
Accuracy: 0.913333333333333 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
Accuracy: 0.956789830926817 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): 0.143 (Std.Error: 0.033)
Trimmed ATE (Yes-No): 0.152 (Std.Error: 0.035)
Upper ATE (Yes-No): -0.101 (Std.Error: 0.035)
Observational differences in treatment 0.046 (Yes-No) 

   treatment    outcome
1:       Yes 0.08888889
2:        No 0.04324324
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: Other complications
Distribution:
Proportion 
 0.0626506 
Model Type Y: boosting 
Accuracy: 0.913333333333333 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
Accuracy: 0.940572627421942 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): 0.027 (Std.Error: 0.018)
Trimmed ATE (Yes-No): 0.03 (Std.Error: 0.018)
Upper ATE (Yes-No): -0.04 (Std.Error: 0.031)
Observational differences in treatment 0.029 (Yes-No) 

   treatment    outcome
1:       Yes 0.08888889
2:        No 0.05945946
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Average Treatment Effects - Reintervention Number

Outcome: reinterventions
Distribution:
  0%  25%  50%  75% 100% 
   0    0    0    1    6 
Model Type Y: boosting 
RMSE: 1.47342708159134 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5714286

Model Type No: boosting 
RMSE: 0.914686702660187 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.7142857

ATE (Yes-No): 0.032 (Std.Error: 0.093)
Trimmed ATE (Yes-No): 0.089 (Std.Error: 0.097)
Upper ATE (Yes-No): -1.483 (Std.Error: 0.128)
Observational differences in treatment 0.135 (Yes-No) 

   treatment   outcome
1:        No 0.4648649
2:       Yes 0.6000000
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Average Treatment Effects - Had Reintervention

Outcome: had_reintervention
Distribution:
Proportion 
 0.2963855 
Model Type Y: boosting 
Accuracy: 0.660555555555556 
Params: nrounds: 50.0
max_depth: 7
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
Accuracy: 0.708123411082315 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.7142857

ATE (Yes-No): 0.053 (Std.Error: 0.04)
Trimmed ATE (Yes-No): 0.049 (Std.Error: 0.041)
Upper ATE (Yes-No): 0.163 (Std.Error: 0.059)
Observational differences in treatment 0.017 (Yes-No) 

   treatment   outcome
1:       Yes 0.3111111
2:        No 0.2945946
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'